Overview

Dataset statistics

Number of variables10
Number of observations800
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory62.6 KiB
Average record size in memory80.2 B

Variable types

DateTime2
Numeric6
Categorical2

Alerts

dropoff_latitude is highly overall correlated with pickup_latitudeHigh correlation
dropoff_longitude is highly overall correlated with pickup_longitudeHigh correlation
fare_amount is highly overall correlated with trip_distance_milesHigh correlation
pickup_latitude is highly overall correlated with dropoff_latitudeHigh correlation
pickup_longitude is highly overall correlated with dropoff_longitudeHigh correlation
trip_distance_miles is highly overall correlated with fare_amountHigh correlation
pickup_latitude has unique valuesUnique

Reproduction

Analysis started2026-01-10 11:56:15.526872
Analysis finished2026-01-10 11:56:18.857086
Duration3.33 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

Distinct798
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
Minimum2023-01-01 04:56:00
Maximum2023-03-02 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2026-01-10T12:56:18.919225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-10T12:56:18.991928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct797
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
Minimum2023-01-01 05:36:00
Maximum2023-03-02 00:09:00
Invalid dates0
Invalid dates (%)0.0%
2026-01-10T12:56:19.060956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-10T12:56:19.132284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

pickup_latitude
Real number (ℝ)

High correlation  Unique 

Distinct800
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.751655
Minimum40.600025
Maximum40.899676
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2026-01-10T12:56:19.198705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum40.600025
5-th percentile40.61418
Q140.679936
median40.750586
Q340.825291
95-th percentile40.885749
Maximum40.899676
Range0.299651
Interquartile range (IQR)0.145355

Descriptive statistics

Standard deviation0.085945463
Coefficient of variation (CV)0.0021090054
Kurtosis-1.1596738
Mean40.751655
Median Absolute Deviation (MAD)0.0730855
Skewness0.012174136
Sum32601.324
Variance0.0073866225
MonotonicityNot monotonic
2026-01-10T12:56:19.268242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.6157741
 
0.1%
40.6343151
 
0.1%
40.7541041
 
0.1%
40.8698171
 
0.1%
40.8506031
 
0.1%
40.8963811
 
0.1%
40.6382171
 
0.1%
40.6795291
 
0.1%
40.7055111
 
0.1%
40.8482971
 
0.1%
Other values (790)790
98.8%
ValueCountFrequency (%)
40.6000251
0.1%
40.6006281
0.1%
40.6008061
0.1%
40.601061
0.1%
40.6016641
0.1%
40.6017831
0.1%
40.6018081
0.1%
40.6022281
0.1%
40.6023211
0.1%
40.6039481
0.1%
ValueCountFrequency (%)
40.8996761
0.1%
40.8988261
0.1%
40.8987581
0.1%
40.8987351
0.1%
40.8986081
0.1%
40.8983831
0.1%
40.897911
0.1%
40.8977441
0.1%
40.896711
0.1%
40.8965041
0.1%

pickup_longitude
Real number (ℝ)

High correlation 

Distinct799
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-73.878843
Minimum-74.049958
Maximum-73.700125
Zeros0
Zeros (%)0.0%
Negative800
Negative (%)100.0%
Memory size6.4 KiB
2026-01-10T12:56:19.338645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-74.049958
5-th percentile-74.035064
Q1-73.972376
median-73.880704
Q3-73.791455
95-th percentile-73.716308
Maximum-73.700125
Range0.349833
Interquartile range (IQR)0.18092125

Descriptive statistics

Standard deviation0.10406351
Coefficient of variation (CV)-0.0014085698
Kurtosis-1.2518287
Mean-73.878843
Median Absolute Deviation (MAD)0.090814
Skewness0.03378927
Sum-59103.074
Variance0.010829214
MonotonicityNot monotonic
2026-01-10T12:56:19.408715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-74.021582
 
0.2%
-73.7533241
 
0.1%
-73.7273281
 
0.1%
-73.8573151
 
0.1%
-73.9561931
 
0.1%
-73.7034161
 
0.1%
-73.9158821
 
0.1%
-73.8072211
 
0.1%
-73.8085171
 
0.1%
-73.8979921
 
0.1%
Other values (789)789
98.6%
ValueCountFrequency (%)
-74.0499581
0.1%
-74.0495231
0.1%
-74.048871
0.1%
-74.0488151
0.1%
-74.0487191
0.1%
-74.048661
0.1%
-74.0485411
0.1%
-74.0473571
0.1%
-74.0472951
0.1%
-74.0472721
0.1%
ValueCountFrequency (%)
-73.7001251
0.1%
-73.7001841
0.1%
-73.700281
0.1%
-73.7005391
0.1%
-73.7012011
0.1%
-73.7017641
0.1%
-73.7020981
0.1%
-73.7021111
0.1%
-73.7028681
0.1%
-73.7028941
0.1%

dropoff_latitude
Real number (ℝ)

High correlation 

Distinct799
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.751367
Minimum40.57128
Maximum40.924537
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2026-01-10T12:56:19.591101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum40.57128
5-th percentile40.611133
Q140.679864
median40.752038
Q340.824134
95-th percentile40.887167
Maximum40.924537
Range0.353257
Interquartile range (IQR)0.1442705

Descriptive statistics

Standard deviation0.087704328
Coefficient of variation (CV)0.0021521812
Kurtosis-1.0305101
Mean40.751367
Median Absolute Deviation (MAD)0.072132
Skewness-0.017099018
Sum32601.094
Variance0.0076920492
MonotonicityNot monotonic
2026-01-10T12:56:19.656894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.7130782
 
0.2%
40.7752851
 
0.1%
40.8361151
 
0.1%
40.7523781
 
0.1%
40.887521
 
0.1%
40.8317481
 
0.1%
40.7634681
 
0.1%
40.6973331
 
0.1%
40.8087241
 
0.1%
40.7057191
 
0.1%
Other values (789)789
98.6%
ValueCountFrequency (%)
40.571281
0.1%
40.5748461
0.1%
40.57661
0.1%
40.5816771
0.1%
40.5817391
0.1%
40.5828471
0.1%
40.5861551
0.1%
40.5873181
0.1%
40.5884871
0.1%
40.5884971
0.1%
ValueCountFrequency (%)
40.9245371
0.1%
40.9191771
0.1%
40.918781
0.1%
40.9187371
0.1%
40.9185431
0.1%
40.9170271
0.1%
40.9163771
0.1%
40.9162561
0.1%
40.9152551
0.1%
40.9136661
0.1%

dropoff_longitude
Real number (ℝ)

High correlation 

Distinct799
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-73.878629
Minimum-74.075956
Maximum-73.678499
Zeros0
Zeros (%)0.0%
Negative800
Negative (%)100.0%
Memory size6.4 KiB
2026-01-10T12:56:19.726943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-74.075956
5-th percentile-74.036507
Q1-73.970998
median-73.884815
Q3-73.78801
95-th percentile-73.713404
Maximum-73.678499
Range0.397457
Interquartile range (IQR)0.18298725

Descriptive statistics

Standard deviation0.10587304
Coefficient of variation (CV)-0.0014330672
Kurtosis-1.1858692
Mean-73.878629
Median Absolute Deviation (MAD)0.0922515
Skewness0.042260149
Sum-59102.903
Variance0.0112091
MonotonicityNot monotonic
2026-01-10T12:56:19.797020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-73.9330162
 
0.2%
-73.7754411
 
0.1%
-73.7469761
 
0.1%
-73.8853851
 
0.1%
-73.936761
 
0.1%
-73.7288471
 
0.1%
-73.8922621
 
0.1%
-73.8343141
 
0.1%
-73.7897181
 
0.1%
-73.9162461
 
0.1%
Other values (789)789
98.6%
ValueCountFrequency (%)
-74.0759561
0.1%
-74.0713671
0.1%
-74.0709051
0.1%
-74.0645351
0.1%
-74.0621841
0.1%
-74.0614541
0.1%
-74.0610081
0.1%
-74.0607321
0.1%
-74.0592941
0.1%
-74.058841
0.1%
ValueCountFrequency (%)
-73.6784991
0.1%
-73.6804431
0.1%
-73.6815611
0.1%
-73.6827581
0.1%
-73.6849951
0.1%
-73.6858281
0.1%
-73.6874151
0.1%
-73.6876131
0.1%
-73.6877241
0.1%
-73.6892541
0.1%

trip_distance_miles
Real number (ℝ)

High correlation 

Distinct335
Distinct (%)41.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5586125
Minimum0.04
Maximum4.54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2026-01-10T12:56:19.866835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.04
5-th percentile0.88
Q11.9
median2.65
Q33.25
95-th percentile4.03
Maximum4.54
Range4.5
Interquartile range (IQR)1.35

Descriptive statistics

Standard deviation0.9312466
Coefficient of variation (CV)0.36396547
Kurtosis-0.47017748
Mean2.5586125
Median Absolute Deviation (MAD)0.67
Skewness-0.28561334
Sum2046.89
Variance0.86722023
MonotonicityNot monotonic
2026-01-10T12:56:19.936979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.019
 
1.1%
2.619
 
1.1%
2.899
 
1.1%
2.868
 
1.0%
2.218
 
1.0%
2.768
 
1.0%
2.577
 
0.9%
2.257
 
0.9%
2.797
 
0.9%
3.367
 
0.9%
Other values (325)721
90.1%
ValueCountFrequency (%)
0.041
0.1%
0.081
0.1%
0.161
0.1%
0.331
0.1%
0.361
0.1%
0.381
0.1%
0.41
0.1%
0.411
0.1%
0.421
0.1%
0.431
0.1%
ValueCountFrequency (%)
4.541
 
0.1%
4.532
0.2%
4.491
 
0.1%
4.482
0.2%
4.461
 
0.1%
4.441
 
0.1%
4.361
 
0.1%
4.333
0.4%
4.291
 
0.1%
4.281
 
0.1%

fare_amount
Real number (ℝ)

High correlation 

Distinct612
Distinct (%)76.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.636225
Minimum3.15
Maximum21.89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2026-01-10T12:56:19.999059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3.15
5-th percentile7.0095
Q110.1975
median12.69
Q315.0375
95-th percentile18.1825
Maximum21.89
Range18.74
Interquartile range (IQR)4.84

Descriptive statistics

Standard deviation3.352882
Coefficient of variation (CV)0.2653389
Kurtosis-0.42107422
Mean12.636225
Median Absolute Deviation (MAD)2.435
Skewness-0.031701882
Sum10108.98
Variance11.241818
MonotonicityNot monotonic
2026-01-10T12:56:20.067402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.85
 
0.6%
10.64
 
0.5%
12.524
 
0.5%
12.994
 
0.5%
12.854
 
0.5%
9.573
 
0.4%
123
 
0.4%
13.883
 
0.4%
13.53
 
0.4%
13.263
 
0.4%
Other values (602)764
95.5%
ValueCountFrequency (%)
3.151
0.1%
3.651
0.1%
4.491
0.1%
4.561
0.1%
4.71
0.1%
5.041
0.1%
5.081
0.1%
5.241
0.1%
5.281
0.1%
5.381
0.1%
ValueCountFrequency (%)
21.891
0.1%
21.381
0.1%
20.881
0.1%
20.761
0.1%
20.681
0.1%
20.011
0.1%
19.931
0.1%
19.561
0.1%
19.521
0.1%
19.491
0.1%

passenger_count
Categorical

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
4
169 
1
165 
5
161 
3
157 
2
148 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters800
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row4
3rd row5
4th row4
5th row3

Common Values

ValueCountFrequency (%)
4169
21.1%
1165
20.6%
5161
20.1%
3157
19.6%
2148
18.5%

Length

2026-01-10T12:56:20.136964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-10T12:56:20.187083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4169
21.1%
1165
20.6%
5161
20.1%
3157
19.6%
2148
18.5%

Most occurring characters

ValueCountFrequency (%)
4169
21.1%
1165
20.6%
5161
20.1%
3157
19.6%
2148
18.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)800
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4169
21.1%
1165
20.6%
5161
20.1%
3157
19.6%
2148
18.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)800
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4169
21.1%
1165
20.6%
5161
20.1%
3157
19.6%
2148
18.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)800
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4169
21.1%
1165
20.6%
5161
20.1%
3157
19.6%
2148
18.5%

payment_type
Categorical

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
Credit Card
524 
Cash
198 
Unknown
 
35
No Charge
 
26
Dispute
 
17

Length

Max length11
Median length11
Mean length8.9425
Min length4

Characters and Unicode

Total characters7154
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCredit Card
2nd rowUnknown
3rd rowCredit Card
4th rowCredit Card
5th rowCredit Card

Common Values

ValueCountFrequency (%)
Credit Card524
65.5%
Cash198
 
24.8%
Unknown35
 
4.4%
No Charge26
 
3.2%
Dispute17
 
2.1%

Length

2026-01-10T12:56:20.244732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-10T12:56:20.286876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
credit524
38.8%
card524
38.8%
cash198
 
14.7%
unknown35
 
2.6%
no26
 
1.9%
charge26
 
1.9%
dispute17
 
1.3%

Most occurring characters

ValueCountFrequency (%)
C1272
17.8%
r1074
15.0%
d1048
14.6%
a748
10.5%
e567
7.9%
550
7.7%
t541
7.6%
i541
7.6%
h224
 
3.1%
s215
 
3.0%
Other values (10)374
 
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)7154
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C1272
17.8%
r1074
15.0%
d1048
14.6%
a748
10.5%
e567
7.9%
550
7.7%
t541
7.6%
i541
7.6%
h224
 
3.1%
s215
 
3.0%
Other values (10)374
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)7154
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C1272
17.8%
r1074
15.0%
d1048
14.6%
a748
10.5%
e567
7.9%
550
7.7%
t541
7.6%
i541
7.6%
h224
 
3.1%
s215
 
3.0%
Other values (10)374
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)7154
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C1272
17.8%
r1074
15.0%
d1048
14.6%
a748
10.5%
e567
7.9%
550
7.7%
t541
7.6%
i541
7.6%
h224
 
3.1%
s215
 
3.0%
Other values (10)374
 
5.2%

Interactions

2026-01-10T12:56:18.309048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-10T12:56:15.940458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-10T12:56:16.471246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-10T12:56:16.976929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-10T12:56:17.394774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-10T12:56:17.951500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-10T12:56:18.367140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-10T12:56:16.036885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-10T12:56:16.552377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-10T12:56:17.042332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-10T12:56:17.486843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-10T12:56:18.011194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-10T12:56:18.449273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-10T12:56:16.137822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-10T12:56:16.674426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-10T12:56:17.112034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-10T12:56:17.576674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-10T12:56:18.078704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-10T12:56:18.516637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-10T12:56:16.227957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-10T12:56:16.770241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-10T12:56:17.182261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-10T12:56:17.651605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-10T12:56:18.137074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-10T12:56:18.582305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-10T12:56:16.328724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-10T12:56:16.841896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-10T12:56:17.248939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-10T12:56:17.816922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-10T12:56:18.197468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-10T12:56:18.642986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-10T12:56:16.390903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-10T12:56:16.899895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-10T12:56:17.310777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-10T12:56:17.876739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-10T12:56:18.249311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-01-10T12:56:20.331708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
dropoff_latitudedropoff_longitudefare_amountpassenger_countpayment_typepickup_latitudepickup_longitudetrip_distance_miles
dropoff_latitude1.0000.019-0.0140.0230.0510.9820.006-0.007
dropoff_longitude0.0191.000-0.0080.0000.0000.0140.986-0.001
fare_amount-0.014-0.0081.0000.0440.040-0.006-0.0100.801
passenger_count0.0230.0000.0441.0000.0390.0000.0000.013
payment_type0.0510.0000.0400.0391.0000.0910.0260.062
pickup_latitude0.9820.014-0.0060.0000.0911.000-0.001-0.004
pickup_longitude0.0060.986-0.0100.0000.026-0.0011.0000.005
trip_distance_miles-0.007-0.0010.8010.0130.062-0.0040.0051.000

Missing values

2026-01-10T12:56:18.741977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-10T12:56:18.806748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

pickup_datetimedropoff_datetimepickup_latitudepickup_longitudedropoff_latitudedropoff_longitudetrip_distance_milesfare_amountpassenger_countpayment_type
02023-02-20 17:27:002023-02-20 17:49:0040.808941-73.91448240.807336-73.9052701.035.845Credit Card
12023-02-28 19:41:002023-02-28 20:07:0040.685842-73.85544940.663358-73.8267454.0220.884Unknown
22023-02-14 08:37:002023-02-14 09:07:0040.668055-74.04505040.642572-74.0556173.0414.145Credit Card
32023-01-16 23:54:002023-01-17 00:40:0040.765394-73.75332440.775285-73.7754412.6711.044Credit Card
42023-02-11 01:16:002023-02-11 01:35:0040.815841-73.72732840.836115-73.7469763.1116.123Credit Card
52023-01-09 06:27:002023-01-09 06:55:0040.726932-73.85731540.752378-73.8853854.1813.504Credit Card
62023-01-11 20:24:002023-01-11 20:41:0040.894229-73.95619340.887520-73.9367602.2710.602Credit Card
72023-01-02 06:40:002023-01-02 07:02:0040.805449-73.70341640.831748-73.7288474.0317.494Cash
82023-01-17 06:44:002023-01-17 07:24:0040.744280-73.91588240.763468-73.8922623.3613.064Credit Card
92023-02-02 03:25:002023-02-02 04:07:0040.717635-73.80722140.713078-73.8343143.0310.194Credit Card
pickup_datetimedropoff_datetimepickup_latitudepickup_longitudedropoff_latitudedropoff_longitudetrip_distance_milesfare_amountpassenger_countpayment_type
7902023-01-26 21:17:002023-01-26 21:38:0040.673873-73.70018440.645738-73.6849953.5316.105Cash
7912023-02-12 03:45:002023-02-12 04:18:0040.661605-73.79655640.674415-73.7963091.4112.521Credit Card
7922023-01-02 15:11:002023-01-02 16:01:0040.805448-73.83214740.778078-73.8344463.0317.023Credit Card
7932023-01-16 12:17:002023-01-16 12:53:0040.745834-73.74441640.744076-73.7363590.915.285Unknown
7942023-02-14 20:47:002023-02-14 21:42:0040.697473-73.93613140.710620-73.9431811.657.481Credit Card
7952023-01-20 11:23:002023-01-20 11:38:0040.630064-73.81660440.616475-73.8101511.667.944Credit Card
7962023-01-10 09:47:002023-01-10 10:19:0040.763429-73.88610240.735097-73.8606274.2017.814Cash
7972023-03-01 14:20:002023-03-01 14:22:0040.704108-74.00661840.679671-73.9853243.5715.972Credit Card
7982023-02-15 09:59:002023-02-15 10:40:0040.717329-73.86062640.695675-73.8463612.8615.901Credit Card
7992023-01-02 14:03:002023-01-02 14:41:0040.693153-73.82883240.703059-73.8064192.7011.621Cash